Abstract Although the overall renal graft survival has dramatically improved over the last several decades, almost half of pediatric patients lose their transplant within 10 years. Epidemiology, immunology and histology are all key elements in understanding post-transplant complications and in predicting clinical outcomes, however accurate prediction of transplant outcomes remains challenging and understudied among pediatric kidney transplant recipients. Machine learning technology is rapidly advancing and has demonstrated remarkable predictive accuracy surpassing classical predictive models in several biomedical applications. The goal of this research proposal is to develop machine learning methods to improve prediction of transplant outcomes through analysis of histology, assemble data sufficient to describe the incidence of different types of rejection and histological findings on pediatric kidney transplant biopsies, and to assess their impact on graft outcome. To achieve these goals, we will assemble an international multicenter cohort of over 700 pediatric kidney transplant recipients and develop and validate machine-learning models combining clinical, immunological and histological data for predicting short-term pediatric graft outcomes. This study will enable accurate, reproducible, and standardized assessment of histopathological findings in pediatric transplant recipients and provide a new method to predict graft outcome. This will pave the way to future large, prospective study assessing the feasibility and potential impact of machine-learning methods in transplant biopsy analysis, with the goal of improving clinical management and resource utilization by identifying patients with high risk of graft loss. Finally, this study will provide critical information for efficient and effective design of clinical trials focused on improving outcomes of pediatric kidney transplant recipients, with an emphasis on individualized treatment.